Diff-SVC is an open-source singing voice conversion tool utilizing diffusion models. It allows users to convert one singing voice into another. The architecture leverages diffusion probabilistic models to generate audio, offering capabilities to modify vocal characteristics such as pitch, timbre, and intonation. Key updates include support for 44.1kHz audio, optimizations for training speed and model size using the 'no_fs2' option, and improved inference. It supports various input and output audio formats. The project aims for academic exchange and is not intended for production environments, with no responsibility for copyright issues arising from generated content. Preprocessing, training, and inference instructions are provided, along with documentation for detailed parameter settings.
Diff-SVC is an open-source singing voice conversion tool utilizing diffusion models.
Explore all tools that specialize in audio synthesis. This domain focus ensures Diff-SVC delivers optimized results for this specific requirement.
Utilizes diffusion probabilistic models to generate high-quality audio, enabling fine-grained control over vocal characteristics.
Supports high-resolution audio processing at 44.1kHz, enhancing audio fidelity and quality.
An optimization that improves training speed and reduces model size without compromising conversion quality.
Automatically slices long audio files for processing, enabling handling of lengthy tracks without manual intervention.
Migrated Hubert inference from ONNX to Torch, enabling GPU-accelerated processing with reduced memory footprint.
Integration with ContentVec models for improved content representation and voice conversion accuracy.
Install Python 3.7 or higher.
Clone the Diff-SVC repository from GitHub.
Install the required dependencies using 'pip install -r requirements.txt'.
Preprocess the audio data using the provided preprocessing scripts.
Configure the training parameters in the 'config.yaml' file.
Run the training script using 'python run.py --config training/config.yaml --exp_name [your project name] --reset'.
Perform inference using the 'inference.ipynb' notebook or the provided inference script.
Download trained models from the QQ channel or Discord for testing.
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"Users praise the tool for its high-quality voice conversion and ease of use, while some mention the need for more detailed documentation."
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